Some of the image data obtained using a radiation-based image acquisition device is basically the scattered radiation. This background is produced when radiation is not absorbed at the examination subject but scattered in the direction of the detector.
It is therefore customary to correct the resulting image datasets in respect of this scattered radiation. For this purpose, in a first step, a scatter background dataset is first determined, the scatter-component-describing data from previous examinations, e.g. from a database, or even from simulations, also being taken into account in addition to the image data itself. Modifications or calculations on the basis of mathematical formulae are also possible. These estimates are well known in the prior art, see e.g. the article by Zellerhoff et al. (Zellerhoff, Scholz, Rührnschopf, Brunnner, “Low contrast 3D-reconstruction from C-arm data”, Proceedings of SPIE, Vol. 5745) which also describes the following subsequent procedure.
As the scatter background dataset is first determined pixel by pixel, it is usual to smooth the entire dataset thereafter by a filter.
Usual methods for estimating the scatter background dataset use an iterative approach, which means that the accuracy of the estimate increases with the number of iterations. Because of the high computational load and the associated time requirement, only a very small number of iteration steps are used in actual methods.
If the scatter background dataset is first determined, a correction step takes place in which the image dataset is corrected pixel by pixel on the basis of the scatter background dataset. For this purpose two variants are basically known. On the one hand, there is subtractive correctionP=T−S, where P is the corrected normalized intensity distribution, T the normalized measured intensity distribution and S the estimated scatter distribution according to the scatter background dataset. The disadvantage of this variant is that—as the scatter background dataset is an estimate—negative values may also occur in P which are self-evidently meaningless. This can lead to massive problems for executing the algorithm.
A second variant, namely multiplicative correction, is therefore frequently used. This follows the formula
  P  =            (              T                  T          +          S                    )        .  
Although this variant has the advantage of always producing positive results, an underestimate of the correction occurs for large ratios, essentially one or greater, of the scatter background data to the image data.
When using this second method, so-called horizontal artifacts occur particularly frequently. This means that e.g. in the case of medical imaging, vessels or organs running in the horizontal direction appear incorrectly or not at all in the 3D reconstruction determined from the corrected image dataset, the horizontal direction being the direction of propagation. These artifacts can be explained by the fact that, in the case of these horizontal paths, extremely heavy attenuation of the signal necessarily occurs at the corresponding pixels, so that a weak signal is overlaid with in some cases greater scatter effects. Ratios of scatter background to actual image signal are often in the range of up to 1.5, but higher values are also possible. On the one hand, this makes estimation difficult, and, on the other, multiplicative correction does not yield correct values here.
Such incorrect reconstruction of radiation-based images, e.g. CT scans, may result in dangerous misdiagnoses, as such high ratios of scatter background to image signal may often arise even in the case of aneurisms for example.